Clustering is a technique used to analyze data either in machine learning, data mining, pattern recognition,image analysis and bioinformatics. So as to produce useful information need for an analysis of data usingclustering process because data has a lot of variety and quantity. In this case the researchers will use the KMeansmethod in which these methods into an efficient and effective algorithms to process data with the varietyand number of lots. K-means algorithm has a problem in determining the best number of clusters. So in thispaper the researchers will conduct research to search for the best number of clusters in K-means method. Thereare many ways to determine this, one of them with methods Elebow. The determination of these methods seenfrom the graph SSE (Sum Square Error) of some number of clusters. Results from this study will be the basis fordetermining the number clusters in the process clustering with K-Means method in a case study, and this casestudy will be conducted at the institute STAHN (Sekolah Tinggi Agama Hindu Negeri) Tampung PenyangPalangkaraya.
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机译：聚类是一种用于在机器学习，数据挖掘，模式识别，图像分析和生物信息学中分析数据的技术。为了产生有用的信息，需要使用聚类过程进行数据分析，因为数据种类繁多且数量众多。在这种情况下，研究人员将使用KMeans方法，将其中的这些方法转化为有效且有效的算法，以处理数量和批次很多的数据。 K-均值算法在确定最佳簇数方面存在问题。因此，在本文中，研究人员将进行研究，以用K均值方法搜索最佳簇数。有很多方法可以确定这一点，其中一种方法是Elebow。从一些群集的图形SSE（平方和误差）中可以看出这些方法的确定。这项研究的结果将成为在案例研究中确定使用K-Means方法进行过程聚类的数量聚类的基础，并且该案例研究将在位于斯坦邦彭扬Palangkaraya的STAHN（Sekolah Tinggi Agama Hindu Negeri）研究所进行。
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